Overview

Dataset statistics

Number of variables34
Number of observations107843
Missing cells188505
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.3 MiB
Average record size in memory265.0 B

Variable types

Numeric16
Boolean1
Unsupported1
Categorical16

Alerts

application_type has constant value "Individual" Constant
desc has a high cardinality: 38454 distinct values High cardinality
earliest_cr_line has a high cardinality: 601 distinct values High cardinality
emp_title has a high cardinality: 68454 distinct values High cardinality
title has a high cardinality: 26875 distinct values High cardinality
zip_code has a high cardinality: 828 distinct values High cardinality
df_index is highly correlated with idHigh correlation
id is highly correlated with df_indexHigh correlation
fico_range_high is highly correlated with fico_range_lowHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
installment is highly correlated with loan_amntHigh correlation
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
revol_bal is highly correlated with loan_amntHigh correlation
total_acc is highly correlated with open_accHigh correlation
df_index is highly correlated with idHigh correlation
id is highly correlated with df_indexHigh correlation
fico_range_high is highly correlated with fico_range_lowHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
installment is highly correlated with loan_amntHigh correlation
loan_amnt is highly correlated with installmentHigh correlation
open_acc is highly correlated with total_accHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
total_acc is highly correlated with open_accHigh correlation
df_index is highly correlated with idHigh correlation
id is highly correlated with df_indexHigh correlation
fico_range_high is highly correlated with fico_range_lowHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
installment is highly correlated with loan_amntHigh correlation
loan_amnt is highly correlated with installmentHigh correlation
pub_rec is highly correlated with pub_rec_bankruptciesHigh correlation
pub_rec_bankruptcies is highly correlated with pub_recHigh correlation
application_type is highly correlated with grade and 10 other fieldsHigh correlation
grade is highly correlated with application_type and 1 other fieldsHigh correlation
sub_grade is highly correlated with application_type and 2 other fieldsHigh correlation
issue_d is highly correlated with application_typeHigh correlation
Charged off is highly correlated with application_typeHigh correlation
home_ownership is highly correlated with application_typeHigh correlation
initial_list_status is highly correlated with application_typeHigh correlation
purpose is highly correlated with application_typeHigh correlation
verification_status is highly correlated with application_typeHigh correlation
emp_length is highly correlated with application_typeHigh correlation
term is highly correlated with application_type and 1 other fieldsHigh correlation
addr_state is highly correlated with application_typeHigh correlation
df_index is highly correlated with id and 1 other fieldsHigh correlation
id is highly correlated with df_index and 1 other fieldsHigh correlation
fico_range_high is highly correlated with fico_range_low and 3 other fieldsHigh correlation
fico_range_low is highly correlated with fico_range_high and 3 other fieldsHigh correlation
grade is highly correlated with int_rate and 1 other fieldsHigh correlation
installment is highly correlated with loan_amntHigh correlation
int_rate is highly correlated with fico_range_high and 4 other fieldsHigh correlation
issue_d is highly correlated with df_index and 1 other fieldsHigh correlation
loan_amnt is highly correlated with installment and 1 other fieldsHigh correlation
open_acc is highly correlated with total_accHigh correlation
revol_util is highly correlated with fico_range_high and 1 other fieldsHigh correlation
sub_grade is highly correlated with fico_range_high and 4 other fieldsHigh correlation
term is highly correlated with int_rate and 2 other fieldsHigh correlation
total_acc is highly correlated with open_accHigh correlation
member_id has 107843 (100.0%) missing values Missing
desc has 68904 (63.9%) missing values Missing
emp_length has 4806 (4.5%) missing values Missing
emp_title has 6889 (6.4%) missing values Missing
annual_inc is highly skewed (γ1 = 21.6135325) Skewed
pub_rec is highly skewed (γ1 = 26.89434201) Skewed
revol_bal is highly skewed (γ1 = 28.52620479) Skewed
df_index is uniformly distributed Uniform
desc is uniformly distributed Uniform
df_index has unique values Unique
id has unique values Unique
member_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
mort_acc has 41241 (38.2%) zeros Zeros
pub_rec has 94951 (88.0%) zeros Zeros
pub_rec_bankruptcies has 96310 (89.3%) zeros Zeros

Reproduction

Analysis started2022-04-10 15:28:52.966449
Analysis finished2022-04-10 15:30:14.679529
Duration1 minute and 21.71 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct107843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67394.81161
Minimum0
Maximum134803
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:14.835221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6728.2
Q133738.5
median67364
Q3101130
95-th percentile128060.9
Maximum134803
Range134803
Interquartile range (IQR)67391.5

Descriptive statistics

Standard deviation38907.88327
Coefficient of variation (CV)0.5773127388
Kurtosis-1.19872438
Mean67394.81161
Median Absolute Deviation (MAD)33699
Skewness0.0003466812915
Sum7268058668
Variance1513823381
MonotonicityNot monotonic
2022-04-10T11:30:15.037331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
232511
 
< 0.1%
187301
 
< 0.1%
725941
 
< 0.1%
282461
 
< 0.1%
1174991
 
< 0.1%
920321
 
< 0.1%
620001
 
< 0.1%
894471
 
< 0.1%
418961
 
< 0.1%
666801
 
< 0.1%
Other values (107833)107833
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
31
< 0.1%
41
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
1348031
< 0.1%
1348021
< 0.1%
1348011
< 0.1%
1348001
< 0.1%
1347991
< 0.1%
1347981
< 0.1%
1347971
< 0.1%
1347961
< 0.1%
1347951
< 0.1%
1347941
< 0.1%

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct107843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6290486.691
Minimum356706
Maximum10234817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:15.249779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum356706
5-th percentile3153397
Q14525573
median6328585
Q37725772.5
95-th percentile9744665.7
Maximum10234817
Range9878111
Interquartile range (IQR)3200199.5

Descriptive statistics

Standard deviation2073355.307
Coefficient of variation (CV)0.3296017318
Kurtosis-0.9953355535
Mean6290486.691
Median Absolute Deviation (MAD)1716133
Skewness-0.01627983342
Sum6.783849562 × 1011
Variance4.298802229 × 1012
MonotonicityNot monotonic
2022-04-10T11:30:15.434783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86056121
 
< 0.1%
85455241
 
< 0.1%
57688331
 
< 0.1%
85750461
 
< 0.1%
33649101
 
< 0.1%
54351371
 
< 0.1%
66465161
 
< 0.1%
56061771
 
< 0.1%
75356841
 
< 0.1%
65649291
 
< 0.1%
Other values (107833)107833
> 99.9%
ValueCountFrequency (%)
3567061
< 0.1%
3800411
< 0.1%
4423191
< 0.1%
4763261
< 0.1%
5469661
< 0.1%
5659351
< 0.1%
5860401
< 0.1%
6219501
< 0.1%
6312991
< 0.1%
6418491
< 0.1%
ValueCountFrequency (%)
102348171
< 0.1%
102348141
< 0.1%
102348131
< 0.1%
102347961
< 0.1%
102347551
< 0.1%
102347501
< 0.1%
102345991
< 0.1%
102345741
< 0.1%
102248341
< 0.1%
102248281
< 0.1%

Charged off
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.4 KiB
False
91024 
True
16819 
ValueCountFrequency (%)
False91024
84.4%
True16819
 
15.6%
2022-04-10T11:30:15.550857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

member_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing107843
Missing (%)100.0%
Memory size842.6 KiB

addr_state
Categorical

HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
CA
17228 
NY
8915 
TX
8258 
FL
7125 
IL
 
4198
Other values (43)
62119 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWV
2nd rowNY
3rd rowCA
4th rowNC
5th rowTX

Common Values

ValueCountFrequency (%)
CA17228
 
16.0%
NY8915
 
8.3%
TX8258
 
7.7%
FL7125
 
6.6%
IL4198
 
3.9%
NJ4083
 
3.8%
PA3660
 
3.4%
OH3446
 
3.2%
GA3383
 
3.1%
VA3274
 
3.0%
Other values (38)44273
41.1%

Length

2022-04-10T11:30:15.644157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca17228
 
16.0%
ny8915
 
8.3%
tx8258
 
7.7%
fl7125
 
6.6%
il4198
 
3.9%
nj4083
 
3.8%
pa3660
 
3.4%
oh3446
 
3.2%
ga3383
 
3.1%
va3274
 
3.0%
Other values (38)44273
41.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct10086
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73218.32335
Minimum6000
Maximum6100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:15.795165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6000
5-th percentile30000
Q145750.06
median63936
Q389000
95-th percentile147810.3
Maximum6100000
Range6094000
Interquartile range (IQR)43249.94

Descriptive statistics

Standard deviation49583.80198
Coefficient of variation (CV)0.6772048267
Kurtosis2116.093894
Mean73218.32335
Median Absolute Deviation (MAD)20064
Skewness21.6135325
Sum7896083645
Variance2458553419
MonotonicityNot monotonic
2022-04-10T11:30:15.989084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600004146
 
3.8%
500003862
 
3.6%
650003138
 
2.9%
400002965
 
2.7%
700002960
 
2.7%
800002868
 
2.7%
450002792
 
2.6%
750002711
 
2.5%
550002698
 
2.5%
1000002051
 
1.9%
Other values (10076)77652
72.0%
ValueCountFrequency (%)
60001
 
< 0.1%
72001
 
< 0.1%
75001
 
< 0.1%
80002
< 0.1%
82001
 
< 0.1%
84001
 
< 0.1%
84121
 
< 0.1%
85002
< 0.1%
85203
< 0.1%
86641
 
< 0.1%
ValueCountFrequency (%)
61000001
 
< 0.1%
20000002
< 0.1%
15100001
 
< 0.1%
13500001
 
< 0.1%
13000001
 
< 0.1%
12500001
 
< 0.1%
12000002
< 0.1%
10500001
 
< 0.1%
10000004
< 0.1%
9900001
 
< 0.1%

application_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
Individual
107843 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual107843
100.0%

Length

2022-04-10T11:30:16.150884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:16.231280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
individual107843
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

desc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct38454
Distinct (%)98.8%
Missing68904
Missing (%)63.9%
Memory size842.6 KiB
Borrower added on 07/25/13 > Debt consolidation<br>
 
6
Borrower added on 09/30/13 > Debt Consolidation<br>
 
5
Borrower added on 09/19/13 > Debt consolidation<br>
 
5
Borrower added on 01/14/13 > Debt consolidation<br>
 
5
Borrower added on 12/16/13 > Debt consolidation<br>
 
4
Other values (38449)
38914 

Length

Max length2365
Median length134
Mean length166.8376435
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38105 ?
Unique (%)97.9%

Sample

1st row Borrower added on 11/05/13 > I need to get a new roof on my house and pay the remainder money on credit cards.<br>
2nd row Borrower added on 12/17/13 > We would like to put in hardwood floors and paint the exterior of the house.<br>
3rd row Borrower added on 01/29/13 > New refrigerator and oven.<br>
4th row Borrower added on 09/09/13 > I need to pay some credit cards off and there a purchase I want to make.<br>
5th row Borrower added on 02/06/13 > I am really tired of being in credit card debt and I want to purchase my first house. By consolidating and paying off all of my debt (other than my car), I can buy a home and not have to worry about credit card debt anymore.<br>

Common Values

ValueCountFrequency (%)
Borrower added on 07/25/13 > Debt consolidation<br>6
 
< 0.1%
Borrower added on 09/30/13 > Debt Consolidation<br>5
 
< 0.1%
Borrower added on 09/19/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 01/14/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 12/16/13 > Debt consolidation<br>4
 
< 0.1%
Borrower added on 12/19/13 > Debt consolidation<br>4
 
< 0.1%
Borrower added on 11/05/13 > Debt Consolidation<br>4
 
< 0.1%
Borrower added on 10/09/13 > Debt consolidation<br>4
 
< 0.1%
Borrower added on 02/27/13 > Debt consolidation<br>4
 
< 0.1%
Borrower added on 12/10/13 > Debt consolidation<br>4
 
< 0.1%
Other values (38444)38894
36.1%
(Missing)68904
63.9%

Length

2022-04-10T11:30:16.356128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on51143
 
4.4%
to48696
 
4.2%
45038
 
3.9%
borrower43999
 
3.8%
added43819
 
3.8%
i34124
 
3.0%
and29148
 
2.5%
credit27868
 
2.4%
my27797
 
2.4%
a22273
 
1.9%
Other values (22445)776840
67.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dti
Real number (ℝ≥0)

Distinct3492
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.21222935
Minimum0
Maximum34.99
Zeros37
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:16.525917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.2
Q111.47
median16.89
Q322.78
95-th percentile30.17
Maximum34.99
Range34.99
Interquartile range (IQR)11.31

Descriptive statistics

Standard deviation7.595111355
Coefficient of variation (CV)0.4412625
Kurtosis-0.683981209
Mean17.21222935
Median Absolute Deviation (MAD)5.64
Skewness0.1324209301
Sum1856218.45
Variance57.68571649
MonotonicityNot monotonic
2022-04-10T11:30:16.703843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.4125
 
0.1%
1892
 
0.1%
15.688
 
0.1%
1282
 
0.1%
19.280
 
0.1%
16.880
 
0.1%
12.7279
 
0.1%
12.4878
 
0.1%
9.677
 
0.1%
16.3277
 
0.1%
Other values (3482)106985
99.2%
ValueCountFrequency (%)
037
< 0.1%
0.012
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.062
 
< 0.1%
0.072
 
< 0.1%
0.082
 
< 0.1%
0.111
 
< 0.1%
0.121
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
34.995
< 0.1%
34.986
< 0.1%
34.978
< 0.1%
34.968
< 0.1%
34.9510
< 0.1%
34.944
 
< 0.1%
34.9310
< 0.1%
34.926
< 0.1%
34.913
 
< 0.1%
34.97
< 0.1%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct601
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
Oct-2000
 
913
Oct-2001
 
846
Oct-1999
 
842
Dec-2000
 
841
Nov-1999
 
827
Other values (596)
103574 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)< 0.1%

Sample

1st rowOct-2001
2nd rowAug-1998
3rd rowFeb-2000
4th rowJun-2000
5th rowMar-1982

Common Values

ValueCountFrequency (%)
Oct-2000913
 
0.8%
Oct-2001846
 
0.8%
Oct-1999842
 
0.8%
Dec-2000841
 
0.8%
Nov-1999827
 
0.8%
Nov-2000814
 
0.8%
Dec-1999766
 
0.7%
Dec-2001761
 
0.7%
Jan-2001761
 
0.7%
Aug-2000760
 
0.7%
Other values (591)99712
92.5%

Length

2022-04-10T11:30:16.882198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oct-2000913
 
0.8%
oct-2001846
 
0.8%
oct-1999842
 
0.8%
dec-2000841
 
0.8%
nov-1999827
 
0.8%
nov-2000814
 
0.8%
dec-1999766
 
0.7%
dec-2001761
 
0.7%
jan-2001761
 
0.7%
aug-2000760
 
0.7%
Other values (591)99712
92.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

emp_length
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing4806
Missing (%)4.5%
Memory size842.6 KiB
10+ years
36566 
2 years
9039 
3 years
8089 
5 years
7779 
< 1 year
7260 
Other values (6)
34304 

Length

Max length9
Median length7
Mean length7.72025583
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 year
2nd row10+ years
3rd row5 years
4th row10+ years
5th row10+ years

Common Values

ValueCountFrequency (%)
10+ years36566
33.9%
2 years9039
 
8.4%
3 years8089
 
7.5%
5 years7779
 
7.2%
< 1 year7260
 
6.7%
6 years6529
 
6.1%
7 years6520
 
6.0%
1 year6179
 
5.7%
4 years5496
 
5.1%
8 years5388
 
5.0%
(Missing)4806
 
4.5%

Length

2022-04-10T11:30:17.024909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years89598
42.0%
1036566
17.1%
113439
 
6.3%
year13439
 
6.3%
29039
 
4.2%
38089
 
3.8%
57779
 
3.6%
7260
 
3.4%
66529
 
3.1%
76520
 
3.1%
Other values (3)15076
 
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct68454
Distinct (%)67.8%
Missing6889
Missing (%)6.4%
Memory size842.6 KiB
Teacher
 
681
Manager
 
544
RN
 
311
Registered Nurse
 
281
US Army
 
249
Other values (68449)
98888 

Length

Max length42
Median length17
Mean length17.59528102
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60694 ?
Unique (%)60.1%

Sample

1st rowDollar General
2nd rowCity and County of San Francisco
3rd rowTREASURER
4th rowGENERAL MANAGER
5th rowForeman

Common Values

ValueCountFrequency (%)
Teacher681
 
0.6%
Manager544
 
0.5%
RN311
 
0.3%
Registered Nurse281
 
0.3%
US Army249
 
0.2%
Supervisor247
 
0.2%
Project Manager202
 
0.2%
Sales176
 
0.2%
Office Manager174
 
0.2%
Bank of America172
 
0.2%
Other values (68444)97917
90.8%
(Missing)6889
 
6.4%

Length

2022-04-10T11:30:17.193989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of6387
 
2.5%
manager5392
 
2.1%
inc5160
 
2.0%
2683
 
1.1%
center2015
 
0.8%
county1912
 
0.8%
services1725
 
0.7%
medical1711
 
0.7%
school1709
 
0.7%
hospital1665
 
0.7%
Other values (31752)221851
88.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fico_range_high
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean699.0001205
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:17.349387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1679
median694
Q3714
95-th percentile754
Maximum850
Range186
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.74380345
Coefficient of variation (CV)0.04112131401
Kurtosis2.437018684
Mean699.0001205
Median Absolute Deviation (MAD)15
Skewness1.369327412
Sum75382270
Variance826.2062369
MonotonicityNot monotonic
2022-04-10T11:30:17.492567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
6749100
 
8.4%
6849011
 
8.4%
6798416
 
7.8%
6698389
 
7.8%
6948275
 
7.7%
6898239
 
7.6%
6647716
 
7.2%
6997486
 
6.9%
7046731
 
6.2%
7096095
 
5.7%
Other values (28)28385
26.3%
ValueCountFrequency (%)
6647716
7.2%
6698389
7.8%
6749100
8.4%
6798416
7.8%
6849011
8.4%
6898239
7.6%
6948275
7.7%
6997486
6.9%
7046731
6.2%
7096095
5.7%
ValueCountFrequency (%)
8508
 
< 0.1%
84415
 
< 0.1%
83924
 
< 0.1%
83440
 
< 0.1%
82969
 
0.1%
82497
0.1%
819126
0.1%
814135
0.1%
809189
0.2%
804200
0.2%

fico_range_low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean695.0000464
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:17.667310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1675
median690
Q3710
95-th percentile750
Maximum845
Range185
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.74341504
Coefficient of variation (CV)0.04135742895
Kurtosis2.435844723
Mean695.0000464
Median Absolute Deviation (MAD)15
Skewness1.369178396
Sum74950890
Variance826.1839079
MonotonicityNot monotonic
2022-04-10T11:30:17.846308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
6709100
 
8.4%
6809011
 
8.4%
6758416
 
7.8%
6658389
 
7.8%
6908275
 
7.7%
6858239
 
7.6%
6607716
 
7.2%
6957486
 
6.9%
7006731
 
6.2%
7056095
 
5.7%
Other values (28)28385
26.3%
ValueCountFrequency (%)
6607716
7.2%
6658389
7.8%
6709100
8.4%
6758416
7.8%
6809011
8.4%
6858239
7.6%
6908275
7.7%
6957486
6.9%
7006731
6.2%
7056095
5.7%
ValueCountFrequency (%)
8458
 
< 0.1%
84015
 
< 0.1%
83524
 
< 0.1%
83040
 
< 0.1%
82569
 
0.1%
82097
0.1%
815126
0.1%
810135
0.1%
805189
0.2%
800200
0.2%

grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
B
35307 
C
30395 
D
16517 
A
14183 
E
7216 
Other values (2)
4225 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowD
4th rowC
5th rowA

Common Values

ValueCountFrequency (%)
B35307
32.7%
C30395
28.2%
D16517
15.3%
A14183
13.2%
E7216
 
6.7%
F3515
 
3.3%
G710
 
0.7%

Length

2022-04-10T11:30:18.005042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:18.096882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
b35307
32.7%
c30395
28.2%
d16517
15.3%
a14183
13.2%
e7216
 
6.7%
f3515
 
3.3%
g710
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

home_ownership
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
MORTGAGE
57648 
RENT
41171 
OWN
9024 

Length

Max length8
Median length8
Mean length6.054542251
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowRENT
3rd rowRENT
4th rowOWN
5th rowOWN

Common Values

ValueCountFrequency (%)
MORTGAGE57648
53.5%
RENT41171
38.2%
OWN9024
 
8.4%

Length

2022-04-10T11:30:18.219366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:18.308938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage57648
53.5%
rent41171
38.2%
own9024
 
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

initial_list_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
f
79060 
w
28783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd roww
3rd roww
4th rowf
5th roww

Common Values

ValueCountFrequency (%)
f79060
73.3%
w28783
 
26.7%

Length

2022-04-10T11:30:18.403742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:18.481211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
f79060
73.3%
w28783
 
26.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

installment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21823
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.7542992
Minimum23.26
Maximum1408.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:18.587701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum23.26
5-th percentile132.75
Q1280.95
median404.97
Q3587.73
95-th percentile921.85
Maximum1408.13
Range1384.87
Interquartile range (IQR)306.78

Descriptive statistics

Standard deviation240.9649629
Coefficient of variation (CV)0.5322201542
Kurtosis0.7452291431
Mean452.7542992
Median Absolute Deviation (MAD)146.23
Skewness0.9000082203
Sum48826381.89
Variance58064.11336
MonotonicityNot monotonic
2022-04-10T11:30:18.772735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337.47318
 
0.3%
635.07298
 
0.3%
317.54296
 
0.3%
332.72292
 
0.3%
332.1280
 
0.3%
328.06278
 
0.3%
343.39277
 
0.3%
476.3255
 
0.2%
625.81246
 
0.2%
327.34239
 
0.2%
Other values (21813)105064
97.4%
ValueCountFrequency (%)
23.261
< 0.1%
25.861
< 0.1%
28.821
< 0.1%
29.521
< 0.1%
29.831
< 0.1%
30.711
< 0.1%
31.31
< 0.1%
31.621
< 0.1%
31.762
< 0.1%
321
< 0.1%
ValueCountFrequency (%)
1408.131
 
< 0.1%
1407.011
 
< 0.1%
1406.454
< 0.1%
1396.791
 
< 0.1%
1391.412
< 0.1%
1388.451
 
< 0.1%
1382.362
< 0.1%
1374.634
< 0.1%
1368.751
 
< 0.1%
1367.653
< 0.1%

int_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53027568
Minimum6
Maximum26.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:18.967681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.62
Q111.14
median14.33
Q317.56
95-th percentile22.47
Maximum26.06
Range20.06
Interquartile range (IQR)6.42

Descriptive statistics

Standard deviation4.439836193
Coefficient of variation (CV)0.305557602
Kurtosis-0.4741654952
Mean14.53027568
Median Absolute Deviation (MAD)3.19
Skewness0.2433553994
Sum1566988.52
Variance19.71214542
MonotonicityNot monotonic
2022-04-10T11:30:19.154554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.94039
 
3.7%
14.333880
 
3.6%
13.113721
 
3.5%
12.123544
 
3.3%
11.143397
 
3.1%
7.92775
 
2.6%
15.82745
 
2.5%
11.992704
 
2.5%
10.992554
 
2.4%
16.292546
 
2.4%
Other values (90)75938
70.4%
ValueCountFrequency (%)
621
 
< 0.1%
6.032032
1.9%
6.621720
1.6%
6.97322
 
0.3%
7.622524
2.3%
7.92775
2.6%
8.6333
 
0.3%
8.94039
3.7%
9.25437
 
0.4%
9.671291
 
1.2%
ValueCountFrequency (%)
26.0644
 
< 0.1%
25.9948
 
< 0.1%
25.8995
 
0.1%
25.83119
0.1%
25.8161
0.1%
25.57125
0.1%
25.28134
0.1%
24.99187
0.2%
24.89239
0.2%
24.8358
 
0.1%

issue_d
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
Dec-2013
12019 
Nov-2013
11760 
Oct-2013
11281 
Sep-2013
10434 
Aug-2013
10173 
Other values (7)
52176 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNov-2013
2nd rowApr-2013
3rd rowSep-2013
4th rowDec-2013
5th rowDec-2013

Common Values

ValueCountFrequency (%)
Dec-201312019
11.1%
Nov-201311760
10.9%
Oct-201311281
10.5%
Sep-201310434
9.7%
Aug-201310173
9.4%
Jul-20139538
8.8%
Jun-20138697
8.1%
May-20138210
7.6%
Apr-20137564
7.0%
Mar-20136649
6.2%
Other values (2)11518
10.7%

Length

2022-04-10T11:30:19.313106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-201312019
11.1%
nov-201311760
10.9%
oct-201311281
10.5%
sep-201310434
9.7%
aug-201310173
9.4%
jul-20139538
8.8%
jun-20138697
8.1%
may-20138210
7.6%
apr-20137564
7.0%
mar-20136649
6.2%
Other values (2)11518
10.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1191
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14719.79336
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:19.458114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile4000
Q18500
median13100
Q320000
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11500

Descriptive statistics

Standard deviation8106.966678
Coefficient of variation (CV)0.5507527505
Kurtosis-0.2023106382
Mean14719.79336
Median Absolute Deviation (MAD)5350
Skewness0.6605167458
Sum1587426675
Variance65722908.71
MonotonicityNot monotonic
2022-04-10T11:30:19.630676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100007885
 
7.3%
120005773
 
5.4%
150005626
 
5.2%
200005570
 
5.2%
80003527
 
3.3%
350003467
 
3.2%
160003211
 
3.0%
180002966
 
2.8%
240002803
 
2.6%
60002684
 
2.5%
Other values (1181)64331
59.7%
ValueCountFrequency (%)
1000278
0.3%
10251
 
< 0.1%
11005
 
< 0.1%
11253
 
< 0.1%
11505
 
< 0.1%
11751
 
< 0.1%
1200167
0.2%
12251
 
< 0.1%
12507
 
< 0.1%
12753
 
< 0.1%
ValueCountFrequency (%)
350003467
3.2%
349755
 
< 0.1%
349001
 
< 0.1%
348009
 
< 0.1%
347501
 
< 0.1%
347001
 
< 0.1%
345751
 
< 0.1%
3450011
 
< 0.1%
3447570
 
0.1%
344002
 
< 0.1%

mort_acc
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.879361665
Minimum0
Maximum31
Zeros41241
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:19.801231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum31
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.193544261
Coefficient of variation (CV)1.167175165
Kurtosis3.357413832
Mean1.879361665
Median Absolute Deviation (MAD)1
Skewness1.484571965
Sum202676
Variance4.811636424
MonotonicityNot monotonic
2022-04-10T11:30:20.267390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
041241
38.2%
118112
16.8%
214648
 
13.6%
311397
 
10.6%
48702
 
8.1%
55826
 
5.4%
63509
 
3.3%
72051
 
1.9%
81090
 
1.0%
9615
 
0.6%
Other values (15)652
 
0.6%
ValueCountFrequency (%)
041241
38.2%
118112
16.8%
214648
 
13.6%
311397
 
10.6%
48702
 
8.1%
55826
 
5.4%
63509
 
3.3%
72051
 
1.9%
81090
 
1.0%
9615
 
0.6%
ValueCountFrequency (%)
311
 
< 0.1%
301
 
< 0.1%
271
 
< 0.1%
243
 
< 0.1%
203
 
< 0.1%
193
 
< 0.1%
183
 
< 0.1%
175
 
< 0.1%
1612
< 0.1%
1517
< 0.1%

open_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.15074692
Minimum0
Maximum62
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:20.429065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q314
95-th percentile20
Maximum62
Range62
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.653656505
Coefficient of variation (CV)0.4173403395
Kurtosis2.086879721
Mean11.15074692
Median Absolute Deviation (MAD)3
Skewness1.016487942
Sum1202530
Variance21.65651886
MonotonicityNot monotonic
2022-04-10T11:30:20.608884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
910701
9.9%
1010324
9.6%
89968
 
9.2%
119538
 
8.8%
78731
 
8.1%
128343
 
7.7%
136996
 
6.5%
66808
 
6.3%
146035
 
5.6%
154842
 
4.5%
Other values (44)25557
23.7%
ValueCountFrequency (%)
03
 
< 0.1%
131
 
< 0.1%
2278
 
0.3%
3913
 
0.8%
42352
 
2.2%
54601
4.3%
66808
6.3%
78731
8.1%
89968
9.2%
910701
9.9%
ValueCountFrequency (%)
621
< 0.1%
532
< 0.1%
521
< 0.1%
511
< 0.1%
501
< 0.1%
492
< 0.1%
481
< 0.1%
461
< 0.1%
452
< 0.1%
441
< 0.1%

pub_rec
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1381638122
Minimum0
Maximum54
Zeros94951
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:20.781445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4767294832
Coefficient of variation (CV)3.450465615
Kurtosis2564.487209
Mean0.1381638122
Median Absolute Deviation (MAD)0
Skewness26.89434201
Sum14900
Variance0.2272710001
MonotonicityNot monotonic
2022-04-10T11:30:20.944430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
094951
88.0%
111665
 
10.8%
2861
 
0.8%
3209
 
0.2%
475
 
0.1%
537
 
< 0.1%
621
 
< 0.1%
713
 
< 0.1%
85
 
< 0.1%
112
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
094951
88.0%
111665
 
10.8%
2861
 
0.8%
3209
 
0.2%
475
 
0.1%
537
 
< 0.1%
621
 
< 0.1%
713
 
< 0.1%
85
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
541
 
< 0.1%
491
 
< 0.1%
112
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%
85
 
< 0.1%
713
 
< 0.1%
621
 
< 0.1%
537
< 0.1%
475
0.1%

pub_rec_bankruptcies
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1099746854
Minimum0
Maximum8
Zeros96310
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:21.083460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3262656157
Coefficient of variation (CV)2.966733793
Kurtosis16.70140439
Mean0.1099746854
Median Absolute Deviation (MAD)0
Skewness3.222420361
Sum11860
Variance0.106449252
MonotonicityNot monotonic
2022-04-10T11:30:21.218132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
096310
89.3%
111287
 
10.5%
2196
 
0.2%
331
 
< 0.1%
414
 
< 0.1%
62
 
< 0.1%
81
 
< 0.1%
51
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
096310
89.3%
111287
 
10.5%
2196
 
0.2%
331
 
< 0.1%
414
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
62
 
< 0.1%
51
 
< 0.1%
414
 
< 0.1%
331
 
< 0.1%
2196
 
0.2%
111287
 
10.5%
096310
89.3%

purpose
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
debt_consolidation
64530 
credit_card
26243 
home_improvement
 
5926
other
 
4647
major_purchase
 
1812
Other values (8)
 
4685

Length

Max length18
Median length18
Mean length15.11400833
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowcredit_card
3rd rowother
4th rowhome_improvement
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation64530
59.8%
credit_card26243
24.3%
home_improvement5926
 
5.5%
other4647
 
4.3%
major_purchase1812
 
1.7%
small_business1087
 
1.0%
car829
 
0.8%
medical722
 
0.7%
house557
 
0.5%
moving502
 
0.5%
Other values (3)988
 
0.9%

Length

2022-04-10T11:30:21.378482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation64530
59.8%
credit_card26243
24.3%
home_improvement5926
 
5.5%
other4647
 
4.3%
major_purchase1812
 
1.7%
small_business1087
 
1.0%
car829
 
0.8%
medical722
 
0.7%
house557
 
0.5%
moving502
 
0.5%
Other values (3)988
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

revol_bal
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct37020
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16812.6326
Minimum0
Maximum2568995
Zeros273
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:21.547093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2976.1
Q17334
median12705
Q321112.5
95-th percentile38945.7
Maximum2568995
Range2568995
Interquartile range (IQR)13778.5

Descriptive statistics

Standard deviation20758.36014
Coefficient of variation (CV)1.234688262
Kurtosis2655.224282
Mean16812.6326
Median Absolute Deviation (MAD)6303
Skewness28.52620479
Sum1813124738
Variance430909515.6
MonotonicityNot monotonic
2022-04-10T11:30:21.745836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0273
 
0.3%
742916
 
< 0.1%
685215
 
< 0.1%
952915
 
< 0.1%
1187414
 
< 0.1%
589614
 
< 0.1%
897914
 
< 0.1%
888114
 
< 0.1%
1136314
 
< 0.1%
933413
 
< 0.1%
Other values (37010)107441
99.6%
ValueCountFrequency (%)
0273
0.3%
14
 
< 0.1%
26
 
< 0.1%
36
 
< 0.1%
47
 
< 0.1%
52
 
< 0.1%
62
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
25689951
< 0.1%
17467161
< 0.1%
6946151
< 0.1%
6178381
< 0.1%
6056271
< 0.1%
5708421
< 0.1%
5098751
< 0.1%
4917121
< 0.1%
4884211
< 0.1%
4819121
< 0.1%

revol_util
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1062
Distinct (%)1.0%
Missing59
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.58248349
Minimum0
Maximum128.1
Zeros290
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:21.945292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.3
Q142.9
median60.3
Q376.3
95-th percentile92.2
Maximum128.1
Range128.1
Interquartile range (IQR)33.4

Descriptive statistics

Standard deviation22.5044375
Coefficient of variation (CV)0.3841495984
Kurtosis-0.5398613939
Mean58.58248349
Median Absolute Deviation (MAD)16.6
Skewness-0.3468060351
Sum6314254.4
Variance506.4497074
MonotonicityNot monotonic
2022-04-10T11:30:22.133598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0290
 
0.3%
61.6209
 
0.2%
67.4204
 
0.2%
64.6204
 
0.2%
67.9203
 
0.2%
65.2203
 
0.2%
72200
 
0.2%
59.3200
 
0.2%
56.4200
 
0.2%
70.8199
 
0.2%
Other values (1052)105672
98.0%
ValueCountFrequency (%)
0290
0.3%
0.135
 
< 0.1%
0.231
 
< 0.1%
0.324
 
< 0.1%
0.424
 
< 0.1%
0.514
 
< 0.1%
0.618
 
< 0.1%
0.724
 
< 0.1%
0.831
 
< 0.1%
0.922
 
< 0.1%
ValueCountFrequency (%)
128.11
< 0.1%
127.61
< 0.1%
122.51
< 0.1%
120.22
< 0.1%
119.21
< 0.1%
115.31
< 0.1%
113.91
< 0.1%
112.71
< 0.1%
109.92
< 0.1%
109.31
< 0.1%

sub_grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
B4
8466 
B3
8231 
B2
7849 
C3
 
6494
B1
 
6261
Other values (30)
70542 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC2
2nd rowC3
3rd rowD2
4th rowC4
5th rowA4

Common Values

ValueCountFrequency (%)
B48466
 
7.9%
B38231
 
7.6%
B27849
 
7.3%
C36494
 
6.0%
B16261
 
5.8%
C46233
 
5.8%
C16103
 
5.7%
C25848
 
5.4%
C55717
 
5.3%
D14502
 
4.2%
Other values (25)42139
39.1%

Length

2022-04-10T11:30:22.300280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b48466
 
7.9%
b38231
 
7.6%
b27849
 
7.3%
c36494
 
6.0%
b16261
 
5.8%
c46233
 
5.8%
c16103
 
5.7%
c25848
 
5.4%
c55717
 
5.3%
d14502
 
4.2%
Other values (25)42139
39.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

term
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
36 months
80314 
60 months
27529 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 60 months
2nd row 36 months
3rd row 36 months
4th row 60 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months80314
74.5%
60 months27529
 
25.5%

Length

2022-04-10T11:30:22.431430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:22.533349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
months107843
50.0%
3680314
37.2%
6027529
 
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

title
Categorical

HIGH CARDINALITY

Distinct26875
Distinct (%)24.9%
Missing4
Missing (%)< 0.1%
Memory size842.6 KiB
Debt consolidation
14837 
Debt Consolidation
 
7170
Credit card refinancing
 
5333
Consolidation
 
2851
debt consolidation
 
2324
Other values (26870)
75324 

Length

Max length40
Median length18
Mean length16.16851974
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22437 ?
Unique (%)20.8%

Sample

1st rowMoney
2nd rowMy Loan
3rd rowOther
4th rowHOME IMPROVEMENT LOAN
5th rowChase-AMEX payoff

Common Values

ValueCountFrequency (%)
Debt consolidation14837
 
13.8%
Debt Consolidation7170
 
6.6%
Credit card refinancing5333
 
4.9%
Consolidation2851
 
2.6%
debt consolidation2324
 
2.2%
Other1467
 
1.4%
Home improvement1230
 
1.1%
consolidation1136
 
1.1%
Credit Card Consolidation1089
 
1.0%
Debt Consolidation Loan863
 
0.8%
Other values (26865)69539
64.5%

Length

2022-04-10T11:30:22.705437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
consolidation38682
 
15.8%
debt37942
 
15.5%
credit19565
 
8.0%
card15675
 
6.4%
loan13284
 
5.4%
refinancing5761
 
2.4%
home4866
 
2.0%
payoff4550
 
1.9%
pay3857
 
1.6%
off3316
 
1.4%
Other values (7812)97315
39.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_acc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.90932188
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.6 KiB
2022-04-10T11:30:22.955407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q117
median23
Q331
95-th percentile46
Maximum105
Range103
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.10443396
Coefficient of variation (CV)0.4457943101
Kurtosis0.5507030228
Mean24.90932188
Median Absolute Deviation (MAD)7
Skewness0.7596197399
Sum2686296
Variance123.3084537
MonotonicityNot monotonic
2022-04-10T11:30:23.210469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224113
 
3.8%
204071
 
3.8%
214058
 
3.8%
234050
 
3.8%
174045
 
3.8%
184003
 
3.7%
193975
 
3.7%
243951
 
3.7%
253709
 
3.4%
263684
 
3.4%
Other values (71)68184
63.2%
ValueCountFrequency (%)
27
 
< 0.1%
368
 
0.1%
4212
 
0.2%
5377
 
0.3%
6663
 
0.6%
7984
0.9%
81307
1.2%
91623
1.5%
102064
1.9%
112337
2.2%
ValueCountFrequency (%)
1051
< 0.1%
981
< 0.1%
881
< 0.1%
841
< 0.1%
831
< 0.1%
821
< 0.1%
802
< 0.1%
792
< 0.1%
781
< 0.1%
751
< 0.1%

verification_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
Verified
52968 
Not Verified
31107 
Source Verified
23768 

Length

Max length15
Median length12
Mean length10.69654961
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowNot Verified
3rd rowNot Verified
4th rowVerified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Verified52968
49.1%
Not Verified31107
28.8%
Source Verified23768
22.0%

Length

2022-04-10T11:30:23.442454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-10T11:30:23.578328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
verified107843
66.3%
not31107
 
19.1%
source23768
 
14.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

zip_code
Categorical

HIGH CARDINALITY

Distinct828
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size842.6 KiB
945xx
 
1306
750xx
 
1161
112xx
 
1124
606xx
 
1054
900xx
 
964
Other values (823)
102234 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row254xx
2nd row136xx
3rd row941xx
4th row277xx
5th row757xx

Common Values

ValueCountFrequency (%)
945xx1306
 
1.2%
750xx1161
 
1.1%
112xx1124
 
1.0%
606xx1054
 
1.0%
900xx964
 
0.9%
100xx933
 
0.9%
331xx926
 
0.9%
300xx910
 
0.8%
070xx908
 
0.8%
917xx863
 
0.8%
Other values (818)97694
90.6%

Length

2022-04-10T11:30:23.729407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
945xx1306
 
1.2%
750xx1161
 
1.1%
112xx1124
 
1.0%
606xx1054
 
1.0%
900xx964
 
0.9%
100xx933
 
0.9%
331xx926
 
0.9%
300xx910
 
0.8%
070xx908
 
0.8%
917xx863
 
0.8%
Other values (818)97694
90.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-10T11:30:07.501531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:20.216255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:23.465368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:27.267885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:30.299338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-04-10T11:29:57.277668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:00.295187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:03.640251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:06.726218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:10.102230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:22.896975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:26.293800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:29.744638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:32.651034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:35.612869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:38.971490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:42.032180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:45.487153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:48.489552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:51.451415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:54.647873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:57.462674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:00.500847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:03.824512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:06.926493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:10.278768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:23.099793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:26.504756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:29.930358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:32.827629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:36.147044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:39.159089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:42.225247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:45.699750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:48.707468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:51.632108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:54.814740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:57.659880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:00.699163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:04.004992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:07.116516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:10.450931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:23.282393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:26.734952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:30.114615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:33.007462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:36.325629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:39.323305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:42.406481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:45.890890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:48.920276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:51.813452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:54.985412image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:29:57.852315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:00.899593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:04.188323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-10T11:30:07.303920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-10T11:30:23.954752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-10T11:30:24.344194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-10T11:30:24.721770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-10T11:30:25.079270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-10T11:30:25.411004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-10T11:30:10.948274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-10T11:30:12.459717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-10T11:30:13.423409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-10T11:30:13.840438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexidCharged offmember_idaddr_stateannual_incapplication_typedescdtiearliest_cr_lineemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinitial_list_statusinstallmentint_rateissue_dloan_amntmort_accopen_accpub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermtitletotal_accverification_statuszip_code
0232518605612TrueNaNWV45000.0IndividualBorrower added on 11/05/13 > I need to get a new roof on my house and pay the remainder money on credit cards.<br>19.63Oct-2001NaNNaN699.0695.0CMORTGAGEw262.2715.10Nov-201311000.05.010.01.01.0debt_consolidation10270.054.3C260 monthsMoney27.0Verified254xx
11089334174682FalseNaNNY26000.0IndividualNaN14.72Aug-19981 yearDollar General689.0685.0CRENTw291.8715.80Apr-20138325.02.010.00.00.0credit_card9528.063.1C336 monthsMy Loan19.0Not Verified136xx
2558506896193FalseNaNCA72000.0IndividualNaN20.70Feb-200010+ yearsCity and County of San Francisco674.0670.0DRENTw145.1218.25Sep-20134000.00.012.00.00.0other14687.071.0D236 monthsOther14.0Not Verified941xx
344759827905FalseNaNNC32000.0IndividualBorrower added on 12/17/13 > We would like to put in hardwood floors and paint the exterior of the house.<br>19.63Jun-20005 yearsTREASURER714.0710.0COWNf241.1215.61Dec-201310000.03.021.00.00.0home_improvement19773.030.1C460 monthsHOME IMPROVEMENT LOAN40.0Verified277xx
4110258978826FalseNaNTX85000.0IndividualNaN24.64Mar-198210+ yearsGENERAL MANAGER739.0735.0AOWNw876.137.90Dec-201328000.01.09.00.00.0debt_consolidation22484.036.7A436 monthsChase-AMEX payoff20.0Source Verified757xx
5308868244741FalseNaNCT52500.0IndividualNaN4.09Dec-200610+ yearsForeman669.0665.0BMORTGAGEf204.6712.99Oct-20136075.02.08.00.00.0credit_card3011.049.4B436 monthshome improvment12.0Not Verified065xx
61270733235259FalseNaNGA120000.0IndividualNaN10.88Apr-2003< 1 yearASVK TECHNOLOGIES734.0730.0BRENTw539.9613.11Feb-201316000.00.07.00.00.0small_business27823.060.7B436 monthsBusiness16.0Source Verified300xx
71110633811042TrueNaNAL30000.0IndividualNaN28.20Dec-1998< 1 yearMandoki Hospitality Inc694.0690.0CRENTf171.7014.33Apr-20135000.00.011.00.00.0credit_card17299.049.1C136 monthscredit card payoff15.0Not Verified365xx
81258303221189FalseNaNAL275000.0IndividualBorrower added on 01/29/13 > New refrigerator and oven.<br>11.52Mar-199810+ yearsGlaxoSmithKline664.0660.0CMORTGAGEf210.3615.80Feb-20136000.07.013.00.00.0major_purchase51680.095.9C336 monthsMajor purchase45.0Source Verified352xx
9195668649956FalseNaNNC40266.0IndividualNaN9.48Jul-19883 yearsNaN674.0670.0DMORTGAGEf308.3318.55Nov-201312000.02.06.01.00.0debt_consolidation8967.070.1D260 monthsMoving forward15.0Verified287xx

Last rows

df_indexidCharged offmember_idaddr_stateannual_incapplication_typedescdtiearliest_cr_lineemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinitial_list_statusinstallmentint_rateissue_dloan_amntmort_accopen_accpub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermtitletotal_accverification_statuszip_code
107833570216828565TrueNaNTX77983.0IndividualBorrower added on 08/19/13 > I will pay off my vehicle (around $10K) and some of my smaller credit cards. Paying off the vehicle will free up almost the exact amount of the payment for this loan so it will have little effect on my monthly budget. I have excellent payment history as my credit report shows. My job is stable.<br>15.96Jul-199710+ yearsDept of Air Force664.0660.0CMORTGAGEw445.2216.78Aug-201318000.01.019.00.00.0debt_consolidation28930.086.9C560 monthsConsolidation Loan24.0Verified764xx
107834460407080854FalseNaNNY90000.0IndividualNaN9.68Jun-200010+ yearsClifford Chance US LLP704.0700.0AOWNw395.188.60Sep-201312500.01.018.00.00.0debt_consolidation28626.040.3A436 monthsPay Credit Cards35.0Not Verified112xx
107835218988755483FalseNaNTX78000.0IndividualBorrower added on 11/07/13 > To be used to pay off higher interest credit cards. This will lower my monthly payments and still be less than what was being paid as a minimum payment on the credit cards and it will be paid back in only 3 years. This will serve to further my efforts to achieve financial freedom.<br>28.88Jun-19941 yearSafety Coordinator694.0690.0BMORTGAGEf589.2210.99Nov-201318000.02.016.00.00.0debt_consolidation9661.061.5B236 monthsDebt consolidation27.0Verified776xx
1078361118233808149FalseNaNTX24000.0IndividualBorrower added on 03/20/13 > I am very close to my goal of being debt free! After speaking with a financial counselor for a few months now, I've decided to take this opportunity to pay off all my small debts and be rid of high interests traps. I look forward to improving my credit score even more.<br>10.90Aug-20016 yearsWalmart729.0725.0DRENTw52.2617.77Apr-20131450.00.010.00.00.0credit_card3321.015.8D136 months2nd Loan41.0Not Verified757xx
107837984944526720TrueNaNPA90000.0IndividualNaN5.57Oct-20007 yearsSysco Foods679.0675.0FMORTGAGEw992.3123.28May-201335000.03.08.00.00.0credit_card16680.071.7F260 monthsCredit card refinancing20.0Verified190xx
107838424897383041FalseNaNMA50000.0IndividualNaN25.99Oct-20022 yearsPublic Defender669.0665.0DRENTf364.2918.55Oct-201310000.00.09.00.00.0moving11441.084.7D236 monthsMoving Loan35.0Not Verified024xx
107839439337071982FalseNaNNJ75000.0IndividualNaN12.11Jun-198710+ yearsAtlanticare Regional Medical Center714.0710.0CRENTw458.3316.20Sep-201313000.07.012.00.00.0credit_card11949.033.9C436 monthsconsolidate35.0Not Verified082xx
1078401086424155147FalseNaNNJ36000.0IndividualBorrower added on 04/01/13 > I have about 13,000 in credit card debt that I've accumulated since I was a student. Paying them off one at a time is a painstaking process that takes too long and is ruining my credit history. I want to consolidate all the cards, pay them all off, and once and for all get rid of my all my debt.<br>17.80Oct-20043 yearsGrinberg & Segal PLLC674.0670.0DRENTw468.4917.77Apr-201313000.00.016.00.00.0debt_consolidation14791.070.4D136 monthsCredit Card Debt Consolidation19.0Not Verified070xx
107841477837275215FalseNaNAZ102000.0IndividualBorrower added on 09/11/13 > Debt Consolidation. Trying to pay off debt in the shortest amount of time, pay as little interest as possible (CC's were crazy) and keep a good credit score.<br>9.86Dec-20012 yearsBlack Box Network Services714.0710.0BMORTGAGEf477.7112.99Sep-201321000.02.08.00.00.0debt_consolidation27631.066.9B460 monthsDebt consolidation17.0Source Verified850xx
10784255919845931TrueNaNVA90000.0IndividualNaN14.85Jul-2004< 1 yearQA Analyst/Performance Test Engineer709.0705.0DMORTGAGEf456.0919.22Dec-201317500.03.018.00.00.0debt_consolidation24862.061.4D460 monthsBill Consolidation32.0Verified201xx